
#general data wrangling tasks
import pandas as pd

#read and visualize spatial data
import geopandas as gpd

#providing basemaps
import contextily as ctx

#give power to plots
import matplotlib.pyplot as plt

# for interactive plots
import plotly.express as px

/opt/conda/lib/python3.9/site-packages/geopandas/_compat.py:106: UserWarning: The Shapely GEOS version (3.9.1-CAPI-1.14.2) is incompatible with the GEOS version PyGEOS was compiled with (3.10.1-CAPI-1.16.0). Conversions between both will be slow.
  warnings.warn(


#name dataset as pd and read/show dataset
dfox=pd.read_csv('updatedoxnard_trisummary2020 - Sheet1-5.csv')


# convert df to gdf
gdfox = gpd.GeoDataFrame(dfox, crs='epsg:4326', geometry=gpd.points_from_xy(dfox.Longitude, dfox.Latitude)) 
#geometry put the longitude and latitide points together in new column as object types
gdfox


gdfox.plot(figsize=(30, 20))
plt.ylabel('Latitude')
plt.xlabel('Longitude')
plt.title('Plot of Oxnard, CA TRI Facilities in 2020')

Text(0.5, 1.0, 'Plot of Oxnard, CA TRI Facilities in 2020')


ox_toxic = gpd.read_file("oxnard_toxic.csv")
#using a shortened data set that inccludes just Oxnard locations we upload our data of Oxnard toxic releases.


ox_toxic['Latitude'] = ox_toxic['Latitude'].astype(float)
ox_toxic['Longitude'] = ox_toxic['Longitude'].astype(float)


facility_by_sector_type_ox = ox_toxic['Industry Sector'].value_counts().reset_index()
facility_by_sector_type_ox
#We begin to produce a bar graph by calling the industry sector column and the counts of each type


facility_by_sector_type_ox.columns=['industry_sector','count']
facility_by_sector_type_ox


fig = px.bar(facility_by_sector_type_ox.head(20),
       x='industry_sector',
       y='count',
       title='Oxnard Toxic Releases by Industry Sector',
        labels={
            'industry_sector':'Industry Sector',
            'count':'Count'
        })

fig.show()


newdata=dfox.drop(['Longitude','Latitude', 'Address', 'Industry Sector', 'Releases (lb)', 'geometry'], axis = 1)
#name new dataframe with name as index and not as"0,1,2.."
dfoxx=newdata.set_index("Name")
dfoxx
#Releases (lb) Mean labels
#10,001-100,000lb=55,000.5lb
#101-10,000lb=5,050.5lb
#>0-100lb=50lb
#0lb=0lb


#querying data
#newdata=dfoxx.loc[['Name'], ['Releases(lb)']]
#plot dataframe with edits as bar graph
#dfoxx['Releases (lb) Mean']=dfoxx['Releases (lb) Mean'].astype(float)
dfoxx.plot(figsize=(10, 6), kind='bar')
#customize graph features to add x and y labels, title, and position graph legend
plt.ylabel('Number of Waste Releases in pounds')
plt.xlabel('Name of Facilities')
plt.title('The Amount of Toxic Waste per TRI Facility')
plt.legend(loc='upper right')
#display customized graph
plt.show()
#this bar graph shows that surveyed White people own  more units than surveyed Black and Asian people


ox_toxic.info

<bound method DataFrame.info of     Longitude   Latitude                                      Name  \
0 -119.183020  34.140350                       New-Indy Oxnard LLC   
1 -119.168445  34.141707           Aluminum Precision Products INC   
2 -119.168110  34.196130                        Cemex Oxnard Plant   
3 -119.147180  34.198100                      Parker Hannifin Corp   
4 -119.130352  34.205928                      PTI Technologies Inc   
5 -119.140427  34.209468     The Procter & Gamble Paper Product CO   
6 -119.163291  34.249140  Southern Counties Oil CO LP DBA SC Fuels   
7 -119.129346  34.199354       Associated Ready Mixed Concrete Inc   
8 -119.143053  34.270536                  Vulcan Materials-Saticoy   

                                        Address              Industry Sector  \
0           5936 Perkins Road, Oxnard, CA 93033                        Paper   
1      1001 MC Wane Bouvelard, Oxnard, CA 93033            Fabricated Metals   
2             548 Diaz Avenue, Oxnard, CA 93030  Nonmetallic Mineral Product   
3            2340 Eastman Ave, Oxnard, CA 93030                    Machinery   
4   501 N Del Norte Boulevard, Oxnard, CA 93030     Transportation Equipment   
5           800 N Rice Avenue, Oxnard, CA 93030                        Paper   
6   3815 East Vineyard Avenue, Oxnard, CA 93036     Petroleum Bulk Terminals   
7  392 S. Del Norte Boulevard, Oxnard, CA 93030  Nonmetallic Mineral Product   
8        6029 Vineyard Avenue, Oxnard, CA 93030                    Petroleum   

  geometry  
0     None  
1     None  
2     None  
3     None  
4     None  
5     None  
6     None  
7     None  
8     None  >


fig = px.scatter_mapbox(ox_toxic,
                        lat='Latitude',
                        lon='Longitude',
                        mapbox_style="stamen-terrain")
fig.show()


fig = px.scatter_mapbox(ox_toxic,
                        lat='Latitude',
                        lon='Longitude',
                        color="Industry Sector",
                        labels={'Industry Sector':'Industry Sector'},
                        mapbox_style="stamen-terrain"
                       )
fig.show()


la_toxic = gpd.read_file("toxic_la_data.csv",GEOM_POSSIBLE_NAMES="geometry", KEEP_GEOM_COLUMNS="NO")


la_toxic.shape

(110, 10)


la_toxic['Latitude'] = la_toxic['Latitude'].astype(float)
la_toxic['Longitude'] = la_toxic['Longitude'].astype(float)
#we transform our latititudes and longitudes into floats


# show me distinct value of charges
facility_by_sector_type = la_toxic.industry_sector.value_counts().reset_index()
facility_by_sector_type


# rename the columns
facility_by_sector_type.columns=['industry_sector','count']
facility_by_sector_type


fig = px.bar(facility_by_sector_type.head(20),
       x='industry_sector',
       y='count',
       title='Los Angeles Toxic Releases by Industry Sector',
        labels={
            'industry_sector':'Industry Sector',
            'count':'Count'
        })

fig.show()


fig = px.scatter_mapbox(la_toxic,
                        lat='Latitude',
                        lon='Longitude',
                        color="industry_sector",
                        labels={'industry_sector':'Industry Sector'},
                        mapbox_style="stamen-terrain"
                       )
fig.show()
#using a shortened data set that inccludes just Los Angeles locations we upload our data of Oxnard toxic releases.
#I had to manually add Industry sector types onto the excel sheet because the downloaded file did not include it


#loading geojson file
gdfox=gpd.read_file('oxnardcensus.geojson')


#plot data
gdfox.plot(figsize=(12,12))

<AxesSubplot:>


# columns to keep
columns_to_keep = ['geoid',
 'name',
 'B03002001',
 'B03002002',
 'B03002003',
 'B03002004',
 'B03002005',
 'B03002006',
 'B03002007',
 'B03002008',
 'B03002009',
 'B03002012',
 'geometry']


# redefine gdf with only columns to keep
gdfox = gdfox[columns_to_keep]


#rename column headers with race
gdfox.columns = ['geoid',
 'name',
 'Total',
 'Non Hispanic',
 'Non Hispanic White',
 'Non Hispanic Black',
 'Non Hispanic American Indian and Alaska Native',
 'Non Hispanic Asian',
 'Non Hispanic Native Hawaiian and Other Pacific Islander',
 'Non Hispanic Some other race',
 'Non Hispanic Two or more races',
 'Hispanic',
 'geometry']


gdfox


#add percent columns for each race
gdfox['Percent Non Hispanic White'] = gdfox['Non Hispanic White']/gdfox['Total']*100
gdfox['Percent Non Hispanic Black'] = gdfox['Non Hispanic Black']/gdfox['Total']*100
gdfox['Percent Non Hispanic American Indian and Alaska Native'] = gdfox['Non Hispanic American Indian and Alaska Native']/gdfox['Total']*100
gdfox['Percent Non Hispanic Asian'] = gdfox['Non Hispanic Asian']/gdfox['Total']*100
gdfox['Percent Non Hispanic Native Hawaiian and Other Pacific Islander'] = gdfox['Non Hispanic Native Hawaiian and Other Pacific Islander']/gdfox['Total']*100
gdfox['Percent Non Hispanic Some other race'] = gdfox['Non Hispanic Some other race']/gdfox['Total']*100
gdfox['Percent Non Hispanic Two or more races'] = gdfox['Non Hispanic Two or more races']/gdfox['Total']*100

/opt/conda/lib/python3.9/site-packages/geopandas/geodataframe.py:1322: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy


#Found census tract that Parker Hannifan Facility is in
#Census Tracts: 
#STATE CODE: 06
#GEOID: 06111004902
#CENTLAT: +34.2084355
#COUNTY CODE: 111
#AREAWATER: 0
#NAME: Census Tract 49.02
#CENTLON: -119.1342637
#TRACT CODE: 004902
#AREALAND: 10177889
#source: https://geocoding.geo.census.gov/geocoder/geographies/address?street=2340+Eastman+Ave&city=Oxnard&state=CA&zip=93030&benchmark=4&vintage=4


#locking census tract 49.02/geoid 06111004902 on index 33 in onxard census data table
parker=gdfox.query('index==33')


parker


#plot data
parker.plot(figsize=(12,12))

<AxesSubplot:>


# for network analysis
import networkx as nx

# for street network analysis
import osmnx as ox

# for basemaps
import contextily as ctx


#looking at Oxnard city and driving distance between toxic facilties and nearest residential areas
address = '2340, Eastman Avenue, Oxnard, Ventura County, California, 93030, United States'
network_type = 'drive'
trip_times = [5, 10, 15, 20] #in minutes
meters_per_minute = 1609 # googled avergae mph per minute and converted to meters


%%time
# see how long it takes this cell to run 

# download the street network
G = ox.graph_from_address(address, network_type=network_type, dist = 2000)

CPU times: user 1.24 s, sys: 35.8 ms, total: 1.27 s
Wall time: 1.28 s


#what is graph is this?
type(G)

networkx.classes.multidigraph.MultiDiGraph


# plot using oxmnx
fig, ax = ox.plot_graph(G,figsize=(12,12))
#shows grid of oxnard


# project our network data to Web Mercator (measurements are in meters)
G = ox.project_graph(G, to_crs='epsg:3857')


# convert nodes and edges to geodataframes
gdfpa_nodes, gdfpa_edges = ox.graph_to_gdfs(G)


gdfpa_nodes.sample(10)


gdfpa_edges.head()


#query only residential roads
#gdfpa_edges=gdfpa_edges.query('highway==residential')
#gdfpa_edges.loc[gdfpa_edges['highway'] == residential]
#Select rows whose column value contains a string

gdfpa_edges.loc[gdfpa_edges['highway'].str.contains('residential')]
gdfpa_edges.plot(figsize=(10,10))

<AxesSubplot:>


# get the bounding box coordinates
minx, miny, maxx, maxy = gdfpa_nodes.geometry.total_bounds
print(minx)
print(miny)
print(maxx)
print(maxy)

-13265721.190172315
4053149.7311939765
-13260910.874600055
4057948.5014720145


# calculate the centroid
centroid_x = (maxx-minx)/2 + minx
centroid_y = (maxy-miny)/2 + miny
print(centroid_x)
print(centroid_y)

-13263316.032386184
4055549.1163329957


# use osmnx's distance.nearest_nodes command to get the id for the nearest node
center_node = ox.distance.nearest_nodes(G,Y=centroid_y,X=centroid_x)
print('The id for the nearest node is ' + str(center_node))

The id for the nearest node is 95565605


# what is this record?
gdfpa_nodes.loc[[center_node]]


# set up the subplot (single plot = ax)
fig, ax = plt.subplots(figsize=(10,10))

# add the edges to ax
gdfpa_edges.plot(ax=ax,
               linewidth=0.5,
               edgecolor='gainsboro', 
               zorder=10)

# add all nodes to ax
gdfpa_nodes.plot(ax=ax, 
               markersize=2, 
               zorder=20)

# add the center node in red also to ax
gdfpa_nodes.loc[[center_node]].plot(ax=ax,
                                  color='b', 
                                  zorder=30)

# no axis
ax.axis('off')

# add a basemap
ctx.add_basemap(ax,source=ctx.providers.CartoDB.Positron)


# a quick look at the data
gdfpa_edges[['osmid','name','highway','length']].sample(5)


# create a new column, calculate the time it takes to travel that edge
gdfpa_edges['drive_time'] = gdfpa_edges['length']/meters_per_minute


gdfpa_edges[['osmid','name','highway','length','drive_time']].sample(10)


# assign a color hex code for each trip time isochrone
iso_colors = ox.plot.get_colors(n=len(trip_times), 
                                cmap='YlOrRd', 
                                start=0, 
                                return_hex=True)


# reverse the sort order so that the darker color matches longer times
trip_times.sort(reverse=True)


# create a list of "zipped" time/colors
time_color = list(zip(trip_times, iso_colors))
time_color

[(20, '#ffffcc'), (15, '#febf5a'), (10, '#f43d25'), (5, '#800026')]


# loop through the list of time/colors
for time,color in list(time_color):
    print('The color for '+str(time)+' minutes is ' + color)

The color for 20 minutes is #ffffcc
The color for 15 minutes is #febf5a
The color for 10 minutes is #f43d25
The color for 5 minutes is #800026


# loop through each trip time and associated color
for time, color in list(time_color):

    # for each trip time, create an egograph of nodes that fall within that distance
    subgraph = nx.ego_graph(G, center_node, radius=time, distance='time')

    print('There are ' + str(len(subgraph.nodes())) + ' nodes within ' + str(time) + ' minutes ')
    
    # for each of those nodes, update the gdf_nodes dataframe and assign it with its associated distance color
    for node in subgraph.nodes():
        gdfpa_nodes.loc[node,'time'] = str(time) + ' mins'
        gdfpa_nodes.loc[node,'color'] = color

There are 389 nodes within 20 minutes 
There are 300 nodes within 15 minutes 
There are 165 nodes within 10 minutes 
There are 36 nodes within 5 minutes


# spot check
gdfpa_nodes[['highway','time','color']].sample(20)


# the NaN values then need to be populated with a valid color
gdfpa_nodes['color'].fillna('#cccccc', inplace=True)
# another spot check: are all values in the color column populated?
gdfpa_nodes.sample(10)


#map it
gdfpa_nodes.plot(figsize=(10,10),
               color=gdfpa_nodes['color'],
               markersize=3)

<AxesSubplot:>


# show only the points within 5 minutes
gdfpa_nodes[gdfpa_nodes['time']=='5 mins']


# put it in a variable
gdfpa_5 = gdfpa_nodes[gdfpa_nodes['time']=='5 mins']


# plot it
gdfpa_5.plot(figsize=(10,10),color=gdfpa_5.color)

<AxesSubplot:>


# dissolve the nodes by time
isochrones = gdfpa_nodes.dissolve("time")
isochrones


# for each row, create a convex hull
isochrones = isochrones.convex_hull.reset_index()
isochrones


# geometry header has been automatically named "0"
# let's rename that
isochrones.columns=['time','geometry']


isochrones.head()


isochrones.plot(figsize=(10,10),alpha=0.2,cmap='plasma')

<AxesSubplot:>


# set up the subplots
fig, ax = plt.subplots(figsize=(10,15))

# add the isochrones
isochrones.plot(alpha=0.4, 
                ax=ax, 
                column='time', 
                cmap='plasma', 
                legend=True,
                zorder=20)

# add the center node in red
gdfpa_nodes.loc[[center_node]].plot(ax=ax,color='b', zorder=30)

# add all nodes
gdfpa_nodes.plot(ax=ax, 
               markersize=1, 
               zorder=10)

# add the edges
gdfpa_edges.plot(ax=ax,
               linewidth=0.5,
               alpha=0.2,
               zorder=10)

# hide the axis
ax.axis('off')

# give it a title
ax.set_title('Driving areas from center of Parker Hannifan Corp Facility')

# add the basemap
ctx.add_basemap(ax,source=ctx.providers.CartoDB.Positron)


#mapped parker hannifan and nodes and edges within driving distance

	geoid	name	Total	Non Hispanic	Non Hispanic White	Non Hispanic Black	Non Hispanic American Indian and Alaska Native	Non Hispanic Asian	Non Hispanic Native Hawaiian and Other Pacific Islander	Non Hispanic Some other race	Non Hispanic Two or more races	Hispanic	geometry
0	14000US06111002500	Census Tract 25, Ventura, CA	5374.0	4419.0	4014.0	44.0	97.0	142.0	0.0	0.0	122.0	955.0	MULTIPOLYGON (((-119.28488 34.27208, -119.2847...
1	14000US06111002901	Census Tract 29.01, Ventura, CA	5665.0	2747.0	1538.0	196.0	0.0	679.0	9.0	0.0	325.0	2918.0	MULTIPOLYGON (((-119.22113 34.22552, -119.2210...
2	14000US06111002905	Census Tract 29.05, Ventura, CA	5433.0	2339.0	1110.0	450.0	0.0	587.0	0.0	10.0	182.0	3094.0	MULTIPOLYGON (((-119.26689 34.23456, -119.2654...
3	14000US06111003010	Census Tract 30.10, Ventura, CA	3226.0	805.0	445.0	82.0	1.0	255.0	4.0	0.0	18.0	2421.0	MULTIPOLYGON (((-119.18629 34.22030, -119.1862...
4	14000US06111003011	Census Tract 30.11, Ventura, CA	5539.0	1004.0	612.0	65.0	0.0	260.0	9.0	0.0	58.0	4535.0	MULTIPOLYGON (((-119.19458 34.22066, -119.1945...
5	14000US06111003012	Census Tract 30.12, Ventura, CA	675.0	13.0	13.0	0.0	0.0	0.0	0.0	0.0	0.0	662.0	MULTIPOLYGON (((-119.19133 34.24133, -119.1907...
6	14000US06111003013	Census Tract 30.13, Ventura, CA	5509.0	1934.0	1188.0	230.0	0.0	310.0	10.0	24.0	172.0	3575.0	MULTIPOLYGON (((-119.19446 34.22956, -119.1944...
7	14000US06111003100	Census Tract 31, Ventura, CA	14299.0	5059.0	2093.0	319.0	94.0	2278.0	92.0	0.0	183.0	9240.0	MULTIPOLYGON (((-119.17739 34.22648, -119.1771...
8	14000US06111003201	Census Tract 32.01, Ventura, CA	4230.0	103.0	32.0	7.0	22.0	15.0	0.0	0.0	27.0	4127.0	MULTIPOLYGON (((-119.17733 34.20163, -119.1773...
9	14000US06111003300	Census Tract 33, Ventura, CA	8956.0	1942.0	1523.0	88.0	19.0	121.0	0.0	0.0	191.0	7014.0	MULTIPOLYGON (((-119.19467 34.20925, -119.1946...
10	14000US06111003605	Census Tract 36.05, Ventura, CA	9631.0	4653.0	2765.0	445.0	0.0	891.0	31.0	60.0	461.0	4978.0	MULTIPOLYGON (((-119.22134 34.18140, -119.2213...
11	14000US06111003608	Census Tract 36.08, Ventura, CA	4024.0	1366.0	771.0	68.0	10.0	270.0	28.0	62.0	157.0	2658.0	MULTIPOLYGON (((-119.20799 34.19046, -119.2079...
12	14000US06111003609	Census Tract 36.09, Ventura, CA	5863.0	5347.0	5112.0	0.0	0.0	116.0	0.0	0.0	119.0	516.0	MULTIPOLYGON (((-119.24867 34.19740, -119.2477...
13	14000US06111003612	Census Tract 36.12, Ventura, CA	4962.0	4040.0	3701.0	75.0	83.0	64.0	0.0	0.0	117.0	922.0	MULTIPOLYGON (((-119.23645 34.17589, -119.2356...
14	14000US06111003700	Census Tract 37, Ventura, CA	6687.0	724.0	228.0	410.0	0.0	86.0	0.0	0.0	0.0	5963.0	MULTIPOLYGON (((-119.19503 34.17741, -119.1950...
15	14000US06111003801	Census Tract 38.01, Ventura, CA	5033.0	348.0	107.0	35.0	0.0	206.0	0.0	0.0	0.0	4685.0	MULTIPOLYGON (((-119.18953 34.18811, -119.1895...
16	14000US06111003802	Census Tract 38.02, Ventura, CA	2849.0	258.0	106.0	107.0	0.0	45.0	0.0	0.0	0.0	2591.0	MULTIPOLYGON (((-119.18668 34.18190, -119.1865...
17	14000US06111003900	Census Tract 39, Ventura, CA	6984.0	872.0	241.0	216.0	0.0	387.0	0.0	0.0	28.0	6112.0	MULTIPOLYGON (((-119.17755 34.17352, -119.1775...
18	14000US06111004000	Census Tract 40, Ventura, CA	7179.0	816.0	365.0	108.0	0.0	298.0	0.0	0.0	45.0	6363.0	MULTIPOLYGON (((-119.17766 34.16246, -119.1776...
19	14000US06111004101	Census Tract 41.01, Ventura, CA	6947.0	1041.0	543.0	216.0	10.0	186.0	0.0	0.0	86.0	5906.0	MULTIPOLYGON (((-119.18618 34.17556, -119.1854...
20	14000US06111004304	Census Tract 43.04, Ventura, CA	5978.0	2594.0	1754.0	433.0	16.0	190.0	66.0	0.0	135.0	3384.0	MULTIPOLYGON (((-119.22142 34.17600, -119.2213...
21	14000US06111004503	Census Tract 45.03, Ventura, CA	5112.0	1409.0	558.0	146.0	0.0	615.0	0.0	0.0	90.0	3703.0	MULTIPOLYGON (((-119.17782 34.14751, -119.1778...
22	14000US06111004504	Census Tract 45.04, Ventura, CA	5777.0	910.0	207.0	119.0	12.0	539.0	0.0	0.0	33.0	4867.0	MULTIPOLYGON (((-119.17777 34.15551, -119.1777...
23	14000US06111004505	Census Tract 45.05, Ventura, CA	2208.0	206.0	144.0	32.0	0.0	13.0	11.0	6.0	0.0	2002.0	MULTIPOLYGON (((-119.18649 34.15483, -119.1864...
24	14000US06111004506	Census Tract 45.06, Ventura, CA	7788.0	605.0	263.0	150.0	19.0	127.0	0.0	0.0	46.0	7183.0	MULTIPOLYGON (((-119.18656 34.14939, -119.1865...
25	14000US06111004600	Census Tract 46, Ventura, CA	2035.0	1753.0	1295.0	134.0	28.0	117.0	19.0	0.0	160.0	282.0	MULTIPOLYGON (((-119.16128 34.11793, -119.1602...
26	14000US06111004704	Census Tract 47.04, Ventura, CA	1372.0	391.0	232.0	48.0	0.0	30.0	10.0	20.0	51.0	981.0	MULTIPOLYGON (((-119.14392 34.16554, -119.1434...
27	14000US06111004710	Census Tract 47.10, Ventura, CA	5707.0	1488.0	539.0	187.0	0.0	453.0	178.0	7.0	124.0	4219.0	MULTIPOLYGON (((-119.16629 34.15484, -119.1662...
28	14000US06111004711	Census Tract 47.11, Ventura, CA	3552.0	1475.0	172.0	20.0	0.0	1148.0	96.0	20.0	19.0	2077.0	MULTIPOLYGON (((-119.16853 34.17336, -119.1682...
29	14000US06111004715	Census Tract 47.15, Ventura, CA	5417.0	1231.0	491.0	144.0	8.0	489.0	0.0	33.0	66.0	4186.0	MULTIPOLYGON (((-119.18892 34.13864, -119.1886...
30	14000US06111004716	Census Tract 47.16, Ventura, CA	5066.0	790.0	255.0	34.0	0.0	478.0	6.0	0.0	17.0	4276.0	MULTIPOLYGON (((-119.16005 34.18335, -119.1600...
31	14000US06111004717	Census Tract 47.17, Ventura, CA	3951.0	476.0	142.0	105.0	0.0	163.0	0.0	0.0	66.0	3475.0	MULTIPOLYGON (((-119.15715 34.17315, -119.1568...
32	14000US06111004901	Census Tract 49.01, Ventura, CA	7229.0	3039.0	1128.0	385.0	0.0	1214.0	19.0	35.0	258.0	4190.0	MULTIPOLYGON (((-119.15973 34.20893, -119.1597...
33	14000US06111004902	Census Tract 49.02, Ventura, CA	5072.0	106.0	94.0	0.0	0.0	12.0	0.0	0.0	0.0	4966.0	MULTIPOLYGON (((-119.15976 34.19728, -119.1597...
34	14000US06111005002	Census Tract 50.02, Ventura, CA	3339.0	209.0	187.0	21.0	0.0	0.0	0.0	0.0	1.0	3130.0	MULTIPOLYGON (((-119.15876 34.22519, -119.1587...
35	14000US06111005003	Census Tract 50.03, Ventura, CA	7261.0	1317.0	975.0	143.0	0.0	88.0	22.0	79.0	10.0	5944.0	MULTIPOLYGON (((-119.17310 34.23200, -119.1729...
36	14000US06111005004	Census Tract 50.04, Ventura, CA	9833.0	4698.0	2716.0	285.0	36.0	1081.0	84.0	0.0	496.0	5135.0	MULTIPOLYGON (((-119.18952 34.24251, -119.1875...
37	14000US06111008600	Census Tract 86, Ventura, CA	7920.0	1517.0	598.0	100.0	11.0	594.0	0.0	0.0	214.0	6403.0	MULTIPOLYGON (((-119.19485 34.19013, -119.1948...
38	14000US06111008700	Census Tract 87, Ventura, CA	6029.0	1387.0	1073.0	65.0	27.0	202.0	0.0	0.0	20.0	4642.0	MULTIPOLYGON (((-119.19468 34.20143, -119.1946...
39	14000US06111008800	Census Tract 88, Ventura, CA	5582.0	1533.0	913.0	37.0	9.0	404.0	0.0	0.0	170.0	4049.0	MULTIPOLYGON (((-119.20829 34.18135, -119.2082...
40	14000US06111008900	Census Tract 89, Ventura, CA	3385.0	489.0	372.0	20.0	0.0	80.0	0.0	0.0	17.0	2896.0	MULTIPOLYGON (((-119.19502 34.17186, -119.1950...
41	14000US06111009100	Census Tract 91, Ventura, CA	4870.0	253.0	68.0	82.0	0.0	50.0	11.0	0.0	42.0	4617.0	MULTIPOLYGON (((-119.17745 34.19018, -119.1774...
42	14000US06111990100	Census Tract 9901, Ventura, CA	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	MULTIPOLYGON (((-119.48652 34.00000, -119.4680...

	y	x	street_count	lon	lat	highway	geometry
osmid
7432299931	4.054560e+06	-1.326483e+07	4	-119.159970	34.191614	NaN	POINT (-13264827.228 4054560.216)
7432299893	4.055005e+06	-1.326480e+07	3	-119.159722	34.194922	NaN	POINT (-13264799.520 4055005.469)
95522891	4.056575e+06	-1.326529e+07	3	-119.164102	34.206586	NaN	POINT (-13265287.155 4056575.325)
3216703754	4.057566e+06	-1.326557e+07	3	-119.166605	34.213946	NaN	POINT (-13265565.833 4057566.012)
95501809	4.057164e+06	-1.326310e+07	4	-119.144425	34.210959	NaN	POINT (-13263096.722 4057163.963)
95642704	4.057153e+06	-1.326542e+07	3	-119.165324	34.210875	NaN	POINT (-13265423.199 4057152.628)
7180138182	4.056327e+06	-1.326240e+07	4	-119.138184	34.204741	NaN	POINT (-13262402.010 4056327.011)
95505714	4.056230e+06	-1.326410e+07	3	-119.153446	34.204019	NaN	POINT (-13264100.935 4056229.802)
95417231	4.053836e+06	-1.326540e+07	3	-119.165128	34.186232	NaN	POINT (-13265401.369 4053835.930)
6081088976	4.056690e+06	-1.326284e+07	3	-119.142078	34.207439	NaN	POINT (-13262835.455 4056690.170)

			osmid	name	highway	oneway	length	geometry	lanes	ref	maxspeed	junction
u	v	key
95173394	95536640	0	[10738055, 10714703]	[Beacon Place, Universe Circle]	residential	False	548.837	LINESTRING (-13265403.150 4053573.653, -132653...	NaN	NaN	NaN	NaN
	95536637	0	10739932	Pacific Avenue	tertiary	False	187.589	LINESTRING (-13265403.150 4053573.653, -132654...	NaN	NaN	NaN	NaN
	95631603	0	10739932	Pacific Avenue	tertiary	False	125.875	LINESTRING (-13265403.150 4053573.653, -132654...	NaN	NaN	NaN	NaN
95536640	95173394	0	[10714703, 10738055]	[Beacon Place, Universe Circle]	residential	False	548.837	LINESTRING (-13264958.830 4053343.275, -132649...	NaN	NaN	NaN	NaN
95536640	95536641	0	743170077	Emerson Avenue	tertiary	False	101.574	LINESTRING (-13264958.830 4053343.275, -132649...	NaN	NaN	NaN	NaN

			osmid	name	highway	length
u	v	key
95598637	95525348	0	[10738115, 10734167]	[Paseo Isla, Paseo Lindo]	residential	175.641
95416548	95549698	0	[10739137, 10740609]	[Del Rey Place, Festivo Street]	residential	302.642
95446801	95665374	0	10741881	Almanor Street	residential	75.768
95527009	95505717	0	[10734289, 10743595]	[Santa Lucia Avenue, Kohala Street]	residential	294.148
95494360	95412874	0	10737855	Williams Drive	residential	137.025

			osmid	name	highway	length	drive_time
u	v	key
95569525	2005492989	0	723726873	North Lombard Street	residential	75.825	0.047126
95588539	3216703662	0	315547683	NaN	residential	10.085	0.006268
95536637	95536639	0	743170077	Emerson Avenue	tertiary	159.435	0.099089
95441061	95565605	0	10739080	Candelaria Road	residential	290.774	0.180717
95494363	95518771	0	10742687	Flora Lane	residential	36.029	0.022392
95536640	95173394	0	[10714703, 10738055]	[Beacon Place, Universe Circle]	residential	548.837	0.341104
95520009	95421425	0	10733769	Cordova Street	residential	311.145	0.193378
95631603	95173394	0	10739932	Pacific Avenue	tertiary	125.875	0.078232
3216701738	95527252	0	[10734416, 10740587]	[Nina Drive, Olivia Drive]	residential	278.353	0.172998
95478409	95494360	0	10737855	Williams Drive	residential	123.126	0.076523

Midterm Presentation: Oxnard and Sites of Toxic Releases¶

Sources/Data¶

Plot Map of Toxic Release Facilities¶

Toxic Releases in Oxnard, categorized by Industry Sector¶

Chart Difference in Waste Release per Facility in Oxnard, CA¶

Toxic Releases in Los Angeles, categorized by Industry Sector¶

Interactive Map of Toxic Releases in Oxnard¶

Interactive Map of Toxic Releases in Los Angeles¶

Mapping the Racial Population Around the TRI Facility with the Largest Releases¶

Mapping the Racial Population Around the TRI Facility with the Largest Releases¶

	Longitude	Latitude	Name	Address	Industry Sector	Releases (lb)	Releases (lb) Mean	geometry
0	-119.183020	34.140350	New-Indy Oxnard LLC	5936 Perkins Road, Oxnard, CA 93033	Paper	10,001-100,000	55000.5	POINT (-119.18302 34.14035)
1	-119.168445	34.141707	Aluminum Precision Products INC	1001 MC Wane Bouvelard, Oxnard, CA 93033	Fabricated Metals	101-10,000	5050.5	POINT (-119.16845 34.14171)
2	-119.168110	34.196130	Cemex Oxnard Plant	548 Diaz Avenue, Oxnard, CA 93030	Nonmetallic Mineral Product	0.00	0.0	POINT (-119.16811 34.19613)
3	-119.147180	34.198100	Parker Hannifin Corp	2340 Eastman Ave, Oxnard, CA 93030	Machinery	10,001-100,000	55000.5	POINT (-119.14718 34.19810)
4	-119.130352	34.205928	PTI Technologies Inc	501 N Del Norte Boulevard, Oxnard, CA 93030	Transportation Equipment	101-10,000	5050.5	POINT (-119.13035 34.20593)
5	-119.140427	34.209468	The Procter & Gamble Paper Product CO	800 N Rice Avenue, Oxnard, CA 93030	Paper	101-10,000	5050.5	POINT (-119.14043 34.20947)
6	-119.163291	34.249140	Southern Counties Oil CO LP DBA SC Fuels	3815 East Vineyard Avenue, Oxnard, CA 93036	Petroleum Bulk Terminals	101-10,000	5050.5	POINT (-119.16329 34.24914)
7	-119.129346	34.199354	Associated Ready Mixed Concrete Inc	392 S. Del Norte Boulevard, Oxnard, CA 93030	Nonmetallic Mineral Product	0.00	0.0	POINT (-119.12935 34.19935)
8	-119.143053	34.270536	Vulcan Materials-Saticoy	6029 Vineyard Avenue, Oxnard, CA 93030	Petroleum	>0-100	50.0	POINT (-119.14305 34.27054)

	index	industry_sector
0	Fabricated Metals	47
1	Chemicals	25
2	Nonmetallic Mineral Product	16
3	Food	5
4	Primary Metals	4
5	Hazardous Waste	3
6	Textiles	2
7	Miscellaneous Manufacturing	2
8	Chemical Wholesalers	2
9	Paper	2
10	Petroleum Bulk Terminals	1
11	Plastics and Rubber	1

	y	x	street_count	lon	lat	highway	geometry
osmid
95565605	4.055551e+06	-1.326309e+07	3	-119.144398	34.198973	NaN	POINT (-13263093.716 4055550.633)

	highway	time	color
osmid
95524476	NaN	15 mins	#febf5a
95499230	NaN	15 mins	#febf5a
95485848	NaN	15 mins	#febf5a
5279120140	NaN	5 mins	#800026
95656680	NaN	20 mins	#ffffcc
95614833	NaN	20 mins	#ffffcc
95440966	NaN	20 mins	#ffffcc
95536641	traffic_signals	10 mins	#f43d25
95469442	NaN	20 mins	#ffffcc
95585754	NaN	20 mins	#ffffcc
95644350	NaN	15 mins	#febf5a
95463678	NaN	15 mins	#febf5a
95575730	NaN	20 mins	#ffffcc
95524477	NaN	15 mins	#febf5a
95463669	NaN	15 mins	#febf5a
95536654	NaN	15 mins	#febf5a
7432299926	NaN	10 mins	#f43d25
3216703684	NaN	20 mins	#ffffcc
95424397	NaN	10 mins	#f43d25
95441072	NaN	5 mins	#800026

	y	x	street_count	lon	lat	highway	geometry	time	color
osmid
3216703684	4.057249e+06	-1.326520e+07	3	-119.163338	34.211594	NaN	POINT (-13265202.141 4057249.400)	20 mins	#ffffcc
95441057	4.055905e+06	-1.326361e+07	3	-119.149055	34.201606	NaN	POINT (-13263612.131 4055905.058)	5 mins	#800026
95485851	4.056352e+06	-1.326554e+07	3	-119.166343	34.204929	NaN	POINT (-13265536.600 4056352.275)	15 mins	#febf5a
95468871	4.057824e+06	-1.326397e+07	3	-119.152246	34.215859	NaN	POINT (-13263967.352 4057823.575)	20 mins	#ffffcc
374104279	4.055254e+06	-1.326091e+07	1	-119.124789	34.196768	NaN	POINT (-13260910.875 4055253.875)	5 mins	#800026
95569525	4.057561e+06	-1.326365e+07	3	-119.149353	34.213905	NaN	POINT (-13263645.293 4057560.587)	15 mins	#febf5a
95478411	4.057453e+06	-1.326417e+07	3	-119.154107	34.213107	stop	POINT (-13264174.539 4057453.083)	15 mins	#febf5a
7180138173	4.056539e+06	-1.326410e+07	3	-119.153472	34.206313	NaN	POINT (-13264103.807 4056538.619)	10 mins	#f43d25
95557175	4.056045e+06	-1.326542e+07	3	-119.165323	34.202647	NaN	POINT (-13265423.077 4056045.133)	15 mins	#febf5a
95440966	4.057456e+06	-1.326443e+07	3	-119.156438	34.213125	NaN	POINT (-13264433.970 4057455.560)	20 mins	#ffffcc

	y	x	street_count	lon	lat	highway	geometry	time	color
osmid
95427040	4.054324e+06	-1.326483e+07	5	-119.159980	34.189861	traffic_signals	POINT (-13264828.252 4054324.354)	5 mins	#800026
897648871	4.054322e+06	-1.326481e+07	4	-119.159817	34.189846	traffic_signals	POINT (-13264810.196 4054322.321)	5 mins	#800026
95441057	4.055905e+06	-1.326361e+07	3	-119.149055	34.201606	NaN	POINT (-13263612.131 4055905.058)	5 mins	#800026
95441061	4.055903e+06	-1.326309e+07	3	-119.144378	34.201588	NaN	POINT (-13263091.468 4055902.582)	5 mins	#800026
95569532	4.056006e+06	-1.326360e+07	3	-119.148984	34.202357	NaN	POINT (-13263604.272 4056006.100)	5 mins	#800026
95565607	4.055555e+06	-1.326361e+07	4	-119.149077	34.199006	NaN	POINT (-13263614.580 4055555.074)	5 mins	#800026
95441072	4.055949e+06	-1.326294e+07	2	-119.143042	34.201931	NaN	POINT (-13262942.767 4055948.708)	5 mins	#800026
95565605	4.055551e+06	-1.326309e+07	3	-119.144398	34.198973	NaN	POINT (-13263093.716 4055550.633)	5 mins	#800026
4723364108	4.055952e+06	-1.326286e+07	4	-119.142329	34.201953	traffic_signals	POINT (-13262863.352 4055951.723)	5 mins	#800026
95441078	4.055948e+06	-1.326261e+07	3	-119.140044	34.201925	NaN	POINT (-13262609.065 4055947.941)	5 mins	#800026
4723364109	4.055952e+06	-1.326285e+07	4	-119.142173	34.201956	traffic_signals	POINT (-13262846.030 4055952.113)	5 mins	#800026
5279120140	4.055938e+06	-1.326120e+07	4	-119.127406	34.201853	NaN	POINT (-13261202.187 4055938.196)	5 mins	#800026
95441090	4.055938e+06	-1.326119e+07	4	-119.127264	34.201852	NaN	POINT (-13261186.368 4055938.102)	5 mins	#800026
95505830	4.056382e+06	-1.326117e+07	4	-119.127077	34.205146	NaN	POINT (-13261165.562 4056381.564)	5 mins	#800026
95449269	4.055264e+06	-1.326286e+07	5	-119.142311	34.196846	traffic_signals	POINT (-13262861.426 4055264.360)	5 mins	#800026
374104337	4.055284e+06	-1.326466e+07	3	-119.158424	34.196991	NaN	POINT (-13264655.039 4055283.862)	5 mins	#800026
95449272	4.055469e+06	-1.326285e+07	3	-119.142216	34.198364	NaN	POINT (-13262850.817 4055468.653)	5 mins	#800026
1969327128	4.055255e+06	-1.326120e+07	4	-119.127386	34.196774	NaN	POINT (-13261199.971 4055254.629)	5 mins	#800026
6413662985	4.054305e+06	-1.326287e+07	3	-119.142402	34.189718	traffic_signals	POINT (-13262871.523 4054305.055)	5 mins	#800026
4723364126	4.055549e+06	-1.326287e+07	4	-119.142350	34.198960	NaN	POINT (-13262865.712 4055548.896)	5 mins	#800026
7432299924	4.055610e+06	-1.326480e+07	3	-119.159693	34.199413	NaN	POINT (-13264796.348 4055609.826)	5 mins	#800026
95505768	4.056520e+06	-1.326359e+07	3	-119.148853	34.206174	NaN	POINT (-13263589.611 4056519.855)	5 mins	#800026
95569526	4.056376e+06	-1.326359e+07	3	-119.148848	34.205103	NaN	POINT (-13263589.088 4056375.709)	5 mins	#800026
95533861	4.056165e+06	-1.326322e+07	3	-119.145559	34.203537	NaN	POINT (-13263222.958 4056164.925)	5 mins	#800026
95531958	4.055547e+06	-1.326398e+07	3	-119.152354	34.198946	NaN	POINT (-13263979.374 4055546.999)	5 mins	#800026
95565617	4.055472e+06	-1.326413e+07	3	-119.153748	34.198390	NaN	POINT (-13264134.553 4055472.166)	5 mins	#800026
95569527	4.056142e+06	-1.326359e+07	3	-119.148876	34.203363	NaN	POINT (-13263592.205 4056141.505)	5 mins	#800026
95562620	4.056154e+06	-1.326284e+07	3	-119.142136	34.203455	NaN	POINT (-13262841.923 4056153.929)	5 mins	#800026
897648486	4.055338e+06	-1.326362e+07	1	-119.149086	34.197394	NaN	POINT (-13263615.582 4055338.155)	5 mins	#800026
7432299908	4.055461e+06	-1.326480e+07	4	-119.159710	34.198308	traffic_signals	POINT (-13264798.251 4055461.156)	5 mins	#800026
95565624	4.055462e+06	-1.326482e+07	3	-119.159863	34.198316	traffic_signals	POINT (-13264815.306 4055462.179)	5 mins	#800026
2196325751	4.055291e+06	-1.326482e+07	4	-119.159880	34.197046	traffic_signals	POINT (-13264817.198 4055291.291)	5 mins	#800026
374104279	4.055254e+06	-1.326091e+07	1	-119.124789	34.196768	NaN	POINT (-13260910.875 4055253.875)	5 mins	#800026
7432299900	4.055292e+06	-1.326480e+07	4	-119.159725	34.197048	traffic_signals	POINT (-13264799.932 4055291.587)	5 mins	#800026
7432299886	4.054561e+06	-1.326481e+07	3	-119.159843	34.191621	NaN	POINT (-13264813.057 4054561.158)	5 mins	#800026
6413662989	4.053971e+06	-1.326287e+07	2	-119.142422	34.187234	NaN	POINT (-13262873.738 4053970.785)	5 mins	#800026

	geometry	y	x	street_count	lon	lat	highway	color
time
10 mins	MULTIPOINT (-13265544.983 4055762.738, -132654...	4.053574e+06	-1.326540e+07	3	-119.165144	34.184283	traffic_signals	#f43d25
15 mins	MULTIPOINT (-13265721.190 4053727.870, -132656...	4.056739e+06	-1.326507e+07	4	-119.162163	34.207801	stop	#febf5a
20 mins	MULTIPOINT (-13265715.402 4056995.566, -132655...	4.056685e+06	-1.326483e+07	3	-119.160021	34.207401	stop	#ffffcc
5 mins	MULTIPOINT (-13264828.252 4054324.354, -132648...	4.054324e+06	-1.326483e+07	5	-119.159980	34.189861	traffic_signals	#800026

	time	0
0	10 mins	POLYGON ((-13265246.413 4053149.731, -13265404...
1	15 mins	POLYGON ((-13263979.819 4053165.650, -13265721...
2	20 mins	POLYGON ((-13265267.151 4056468.827, -13265535...
3	5 mins	POLYGON ((-13262873.738 4053970.785, -13264828...